Plant-derived mPGES-1 inhibitors or suppressors: A new emerging trend in the search for small molecules to combat inflammation

Discovering the Active Ingredients of Medicine and Food Homologous Substances for Inhibiting the Cyclooxygenase-2 Metabolic Pathway by Machine Learning Algorithms

Cyclooxygenase-2 (COX-2) and microsomal prostaglandin E2 synthase (mPGES-1) are two key targets in anti-inflammatory therapy. Medicine and food homology (MFH) substances have both edible and medicinal properties, providing a valuable resource for the development of novel, safe, and efficient COX-2 and mPGES-1 inhibitors. In this study, we collected active ingredients from 503 MFH substances and constructed the first comprehensive MFH database containing 27,319 molecules. Subsequently, we performed Murcko scaffold analysis and K-means clustering to deeply analyze the composition of the constructed database and evaluate its structural diversity. Furthermore, we employed four supervised machine learning algorithms, including support vector machine (SVM), random forest (RF), deep neural networks (DNNs), and eXtreme Gradient Boosting (XGBoost), as well as ensemble learning, to establish 640 classification models and 160 regression models for COX-2 and mPGES-1 inhibitors. Among them, ModelA_ensemble_RF_1 emerged as the optimal classification model for COX-2 inhibitors, achieving predicted Matthews correlation coefficient (MCC) values of 0.802 and 0.603 on the test set and external validation set, respectively. ModelC_RDKIT_SVM_2 was identified as the best regression model based on COX-2 inhibitors, with root mean squared error (RMSE) values of 0.419 and 0.513 on the test set and external validation set, respectively. ModelD_ECFP_SVM_4 stood out as the top classification model for mPGES-1 inhibitors, attaining MCC values of 0.832 and 0.584 on the test set and external validation set, respectively. The optimal regression model for mPGES-1 inhibitors, ModelF_3D_SVM_1, exhibited predictive RMSE values of 0.253 and 0.35 on the test set and external validation set, respectively. Finally, we proposed a ligand-based cascade virtual screening strategy, which integrated the well-performing supervised machine learning models with unsupervised learning: the self-organized map (SOM) and molecular scaffold analysis. Using this virtual screening workflow, we discovered 10 potential COX-2 inhibitors and 15 potential mPGES-1 inhibitors from the MFH database. We further verified candidates by molecular docking, investigated the interaction of the candidate molecules upon binding to COX-2 or mPGES-1. The constructed comprehensive MFH database has laid a solid foundation for the further research and utilization of the MFH substances. The series of well-performing machine learning models can be employed to predict the COX-2 and mPGES-1 inhibitory capabilities of unknown compounds, thereby aiding in the discovery of anti-inflammatory medications. The COX-2 and mPGES-1 potential inhibitor molecules identified through the cascade virtual screening approach provide insights and references for the design of highly effective and safe novel anti-inflammatory drugs.

Prediction of bioactivities of microsomal prostaglandin E2 synthase-1 inhibitors by machine learning algorithms

There is a strong interest in the development of microsomal prostaglandin E2 synthase-1 (mPGES-1) inhibitors of their potential to safely and effectively treat inflammation. Herein, 70 QSAR models were built on the dataset (735 mPGES-1 inhibitors) characterized with RDKit descriptors by multiple linear regression (MLR), support vector machine (SVM), random forest (RF), deep neural networks (DNN), and eXtreme Gradient Boosting (XGBoost). The other three regression models on the dataset are represented by SMILES using self-attention recurrent neural networks (RNN) and Graph Convolutional Networks (GCN). For the best model (Model C2), which was developed by SVM with RDKit descriptors, the coefficient of determination (R² ) of 0.861 and root mean squared error (RMSE) of 0.235 were achieved for the test set. Additionally, R² of 0.692 and RMSE of 0.383 were obtained on the external test set. We investigated the applicability domain (AD) of Model C2 with the rivality index (RI), the prediction of Model C2 on 78.92% of molecules in the test set, and 78.33% of molecules in the external test set were reliable. After dissecting the RDKit descriptors of Model C2, we found important physicochemical properties of highly active mPGES-1 inhibitors. Besides, by analyzing the attention weight of each atom of each inhibitor from the attention layer, we found that the benzamide group and the trifluoromethyl cyclohexane group are favorable substructures for mPGES-1 inhibitors.

Alkaloids and Colon Cancer: Molecular Mechanisms and Therapeutic Implications for Cell Cycle Arrest

Cancer is the second most fatal disease worldwide, with colon cancer being the third most prevalent and fatal form of cancer in several Western countries. The risk of acquisition of resistance to chemotherapy remains a significant hurdle in the management of various types of cancer, especially colon cancer. Therefore, it is essential to develop alternative treatment modalities. Naturally occurring alkaloids have been shown to regulate various mechanistic pathways linked to cell proliferation, cell cycle, and metastasis. This review aims to shed light on the potential of alkaloids as anti-colon-cancer chemotherapy agents that can modulate or arrest the cell cycle. Preclinical investigated alkaloids have shown anti-colon cancer activities and inhibition of cancer cell proliferation via cell cycle arrest at different stages, suggesting that alkaloids may have the potential to act as anticancer molecules.

RNAseq analysis of treatment-dependent signaling changes during inflammation in a mouse cutaneous wound healing model

BACKGROUND

Despite proven therapeutic effects in inflammatory conditions, the specific mechanisms of phytochemical therapies are not well understood. The transcriptome effects of Traumeel (Tr14), a multicomponent natural product, and diclofenac, a non-selective cyclooxygenase (COX) inhibitor, were compared in a mouse cutaneous wound healing model to identify both known and novel pathways for the anti-inflammatory effect of plant-derived natural products.

METHODS

Skin samples from abraded mice were analyzed by single-molecule, amplification-free RNAseq transcript profiling at 7 points between 12 and 192 h after injury. Immediately after injury, the wounds were treated with either diclofenac, Tr14, or placebo control (n = 7 per group/time). RNAseq levels were compared between treatment and control at each time point using a systems biology approach.

RESULTS

At early time points (12-36 h), both control and Tr14-treated wounds showed marked increase in the inducible COX2 enzyme mRNA, while diclofenac-treated wounds did not. Tr14, in contrast, modulated lipoxygenase transcripts, especially ALOX12/15, and phospholipases involved in arachidonate metabolism. Notably, Tr14 modulated a group of cell-type specific markers, including the T cell receptor, that could be explained by an overarching effect on the type of cells that were recruited into the wound tissue.

CONCLUSIONS

Tr14 and diclofenac had very different effects on the COX/LOX synthetic pathway after cutaneous wounding. Tr14 allowed normal autoinduction of COX2 mRNA, but suppressed mRNA levels for key enzymes in the leukotriene synthetic pathway. Tr14 appeared to have a broad 'phytocellular' effect on the wound transcriptome by altering the balance of cell types present in the wound.

A Representative GIIA Phospholipase A2 Activates Preadipocytes to Produce Inflammatory Mediators Implicated in Obesity Development

Adipose tissue secretes proinflammatory mediators which promote systemic and adipose tissue inflammation seen in obesity. Group IIA (GIIA)-secreted phospholipase A₂ (sPLA₂) enzymes are found to be elevated in plasma and adipose tissue from obese patients and are active during inflammation, generating proinflammatory mediators, including prostaglandin E₂ (PGE₂). PGE₂ exerts anti-lipolytic actions and increases triacylglycerol levels in adipose tissue. However, the inflammatory actions of GIIA sPLA₂s in adipose tissue cells and mechanisms leading to increased PGE₂ levels in these cells are unclear. This study investigates the ability of a representative GIIA sPLA₂, MT-III, to activate proinflammatory responses in preadipocytes, focusing on the biosynthesis of prostaglandins, adipocytokines and mechanisms involved in these effects. Our results showed that MT-III induced biosynthesis of PGE₂, PGI₂, MCP-1, IL-6 and gene expression of leptin and adiponectin in preadipocytes. The MT-III-induced PGE₂ biosynthesis was dependent on cytosolic PLA₂ (cPLA₂)-α, cyclooxygenases (COX)-1 and COX-2 pathways and regulated by a positive loop via the EP₄ receptor. Moreover, MT-III upregulated COX-2 and microsomal prostaglandin synthase (mPGES)-1 protein expression. MCP-1 biosynthesis induced by MT-III was dependent on the EP4 receptor, while IL-6 biosynthesis was dependent on EP3 receptor engagement by PGE₂. These data highlight preadipocytes as targets for GIIA sPLA₂s and provide insight into the roles played by this group of sPLA₂s in obesity.

The analgesic potential of glycosides derived from medicinal plants

Pain represents an unpleasant sensation linked to actual or potential tissue damage. In the early phase, the sensation of pain is caused due to direct stimulation of the sensory nerve fibers. On the other hand, the pain in the late phase is attributed to inflammatory mediators. Current medicines used to treat inflammation and pain are effective; however, they cause severe side effects, such as ulcer, anemia, osteoporosis, and endocrine disruption. Increased attention is recently being focused on the examination of the analgesic potential of phytoconstituents, such as glycosides of traditional medicinal plants, because they often have suitable biological activities with fewer side effects as compared to synthetic drugs. The purpose of this article is to review for the first time the current state of knowledge on the use of glycosides from medicinal plants to induce analgesia and anti-inflammatory effect. Various databases and search engines, including PubMed, ScienceDirect, Scopus, Web of Science and Google Scholar, were used to search and collect relevant studies on glycosides with antinociceptive activities. The results led to the identification of several glycosides that exhibited marked inhibition of various pain mediators based on different well-established assays. Additionally, these glycosides were found to induce most of the analgesic effects through cyclooxygenase and lipoxygenase pathways. These findings can be useful to identify new candidates which can be clinically developed as analgesics with better bioavailability and reduced side effects. Graphical abstract Analgesic mechanisms of plant glycosides.